Top Modules Exploratory Analysis
#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')
library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(dendextend)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
library(expss)
library(polycor)
library(foreach) ; library(doParallel)
library(knitr)
library(biomaRt)
library(anRichment) ; library(BrainDiseaseCollection)
suppressWarnings(suppressMessages(library(WGCNA)))
SFARI_colour_hue = function(r) {
pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}Load preprocessed dataset (preprocessing code in 19_10_14_data_preprocessing.Rmd) and clustering (pipeline in 19_10_21_WGCNA.Rmd)
# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame
# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>%
mutate('ID'=as.character(ensembl_gene_id)) %>%
dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
mutate('Neuronal'=1)
# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]
# Clusterings
clusterings = read_csv('./../Data/clusters.csv')
# Update DE_info with SFARI and Neuronal information
genes_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>%
mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
left_join(GO_neuronal, by='ID') %>% left_join(clusterings, by='ID') %>%
mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`),
significant=padj<0.05 & !is.na(padj))
# Add gene symbol
getinfo = c('ensembl_gene_id','external_gene_id')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl',
host='feb2014.archive.ensembl.org') ## Gencode v19
gene_names = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=genes_info$ID, mart=mart)
genes_info = genes_info %>% left_join(gene_names, by=c('ID'='ensembl_gene_id'))
clustering_selected = 'DynamicHybrid'
genes_info$Module = genes_info[,clustering_selected]
dataset = read.csv(paste0('./../Data/dataset_', clustering_selected, '.csv'))
dataset$Module = dataset[,clustering_selected]
rm(DE_info, GO_annotations, clusterings, getinfo, mart, dds)Relation to external clinical traits
Quantifying module-trait associations
Using the hetcor function, that calculates Pearson, polyserial or polychoric correlations depending on the type of variables involved.
datTraits = datMeta %>% dplyr::select(Diagnosis, Region, Sex, Age, PMI, RNAExtractionBatch) %>%
dplyr::rename('ExtractionBatch' = RNAExtractionBatch)
# Recalculate MEs with color labels
ME_object = datExpr %>% t %>% moduleEigengenes(colors = genes_info$Module)
MEs = orderMEs(ME_object$eigengenes)
# Calculate correlation between eigengenes and the traits and their p-values
moduleTraitCor = MEs %>% apply(2, function(x) hetcor(x, datTraits)$correlations[1,-1]) %>% t
rownames(moduleTraitCor) = colnames(MEs)
colnames(moduleTraitCor) = colnames(datTraits)
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nrow(datExpr))
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
# In case there are any NAs
if(sum(!complete.cases(moduleTraitCor))>0){
print(paste0(sum(is.na(moduleTraitCor)),' correlation(s) could not be calculated'))
}
rm(ME_object)I’m going to select all the modules that have an absolute correlation higher than 0.9 with Diagnosis to study them
# Sort moduleTraitCor by Diagnosis
moduleTraitCor = moduleTraitCor[order(moduleTraitCor[,1], decreasing=TRUE),]
moduleTraitPvalue = moduleTraitPvalue[order(moduleTraitCor[,1], decreasing=TRUE),]
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = moduleTraitCor, xLabels = names(datTraits), yLabels = gsub('ME','',rownames(moduleTraitCor)),
yColorWidth=0, colors = brewer.pal(11,'PiYG'), bg.lab.y = gsub('ME','',rownames(moduleTraitCor)),
textMatrix = textMatrix, setStdMargins = FALSE, cex.text = 0.8, cex.lab.y = 0.75, zlim = c(-1,1),
main = paste('Module-Trait relationships'))
diagnosis_cor = data.frame('Module' = gsub('ME','',rownames(moduleTraitCor)),
'MTcor' = moduleTraitCor[,'Diagnosis'],
'MTpval' = moduleTraitPvalue[,'Diagnosis'])
genes_info = genes_info %>% left_join(diagnosis_cor, by='Module')
rm(moduleTraitPvalue, datTraits, textMatrix, diagnosis_cor)Studying the modules with the highest absolute correlation to Diagnosis
The modules consist mainly of points with very high (absolute) values in PC2 (which we know is related to lfc), so this result is consistent with the high correlation between Module and Diagnosis, although some of the points with the highest PC2 values do not belong to these top modules
top_modules = gsub('ME','',rownames(moduleTraitCor)[abs(moduleTraitCor[,'Diagnosis'])>0.9])
cat(paste0('Top modules selected: ', paste(top_modules, collapse=', '),'\n'))## Top modules selected: #73B000, #EE8045, #00B9E3, #84AD00pca = datExpr %>% prcomp
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, PC1, PC2, Module, gene.score) %>%
mutate(ImportantModules = ifelse(Module %in% top_modules, as.character(Module), 'Others')) %>%
mutate(color = ifelse(ImportantModules=='Others','gray',ImportantModules),
alpha = ifelse(ImportantModules=='Others', 0.2, 0.4),
gene_id = paste0(ID, ' (', external_gene_id, ')'))
table(plot_data$ImportantModules)##
## #00B9E3 #73B000 #84AD00 #EE8045 Others
## 1733 1461 891 884 11178ggplotly(plot_data %>% ggplot(aes(PC1, PC2, color=ImportantModules)) +
geom_point(alpha=plot_data$alpha, color=plot_data$color, aes(ID=gene_id)) + theme_minimal() +
ggtitle('Modules with strongest relation to Diagnosis'))rm(pca)Module Membership vs Gene Significance
create_plot = function(module){
plot_data = dataset %>% dplyr::select(ID, paste0('MM.',gsub('#','',module)), GS, gene.score) %>% filter(dataset$Module==module)
colnames(plot_data)[2] = 'Module'
SFARI_colors = as.numeric(names(table(as.character(plot_data$gene.score)[plot_data$gene.score!='None'])))
p = ggplotly(plot_data %>% ggplot(aes(Module, GS, color=gene.score)) + geom_point(alpha=0.5, aes(ID=ID)) + ylab('Gene Significance') +
scale_color_manual(values=SFARI_colour_hue(r=c(SFARI_colors,8))) + theme_minimal() + xlab('Module Membership') +
ggtitle(paste0('Module ', module,' (MTcor = ', round(moduleTraitCor[paste0('ME',module),1],2),')')))
return(p)
}
create_plot(top_modules[1])create_plot(top_modules[2])create_plot(top_modules[3])create_plot(top_modules[4])rm(create_plot)SFARI Genes
List of top SFARI Genes in top modules ordered by SFARI score and Gene Significance
table_data = dataset %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, GS, gene.score, Module) %>% arrange(gene.score, desc(abs(GS))) %>%
dplyr::rename('Ensembl ID'=ID, 'Gene Symbol'=external_gene_id,
'SFARI score'=gene.score, 'Gene Significance'=GS)
kable(table_data %>% filter(Module == top_modules[1] & `SFARI score` %in% c(1,2,3)) %>% dplyr::select(-Module),
caption=paste0('Top SFARI Genes for Module ', top_modules[1]))| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000189056 | RELN | -0.1016272 | 1 |
| ENSG00000169432 | SCN9A | 0.4140399 | 2 |
| ENSG00000165186 | PTCHD1 | 0.3027356 | 2 |
| ENSG00000083168 | KAT6A | 0.5999494 | 3 |
| ENSG00000205581 | HMGN1 | 0.5655605 | 3 |
| ENSG00000158321 | AUTS2 | 0.5297062 | 3 |
| ENSG00000259207 | ITGB3 | 0.4676222 | 3 |
| ENSG00000149571 | KIRREL3 | 0.3970895 | 3 |
| ENSG00000113742 | CPEB4 | 0.3724287 | 3 |
| ENSG00000196839 | ADA | 0.3686762 | 3 |
| ENSG00000132510 | KDM6B | 0.3312280 | 3 |
| ENSG00000146938 | NLGN4X | 0.2547162 | 3 |
| ENSG00000118432 | CNR1 | 0.2248206 | 3 |
| ENSG00000168036 | CTNNB1 | 0.1598633 | 3 |
| ENSG00000166148 | AVPR1A | 0.1138557 | 3 |
| ENSG00000044090 | CUL7 | 0.0391425 | 3 |
| ENSG00000151623 | NR3C2 | -0.0150334 | 3 |
kable(table_data %>% filter(Module == top_modules[2] & `SFARI score` %in% c(1,2,3)) %>% dplyr::select(-Module),
caption=paste0('Top SFARI Genes for Module ', top_modules[2]))| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000110066 | SUV420H1 | 0.6536202 | 1 |
| ENSG00000141431 | ASXL3 | 0.5017802 | 1 |
| ENSG00000038382 | TRIO | 0.6260413 | 2 |
| ENSG00000141027 | NCOR1 | 0.4643907 | 2 |
| ENSG00000204764 | RANBP17 | 0.2998930 | 2 |
| ENSG00000168769 | TET2 | 0.8170356 | 3 |
| ENSG00000181090 | EHMT1 | 0.7732158 | 3 |
| ENSG00000162946 | DISC1 | 0.6648966 | 3 |
| ENSG00000197724 | PHF2 | 0.3656794 | 3 |
| ENSG00000145020 | AMT | 0.3410208 | 3 |
| ENSG00000101004 | NINL | 0.2768068 | 3 |
| ENSG00000130940 | CASZ1 | 0.2636101 | 3 |
| ENSG00000128573 | FOXP2 | 0.2493147 | 3 |
| ENSG00000070047 | PHRF1 | 0.2458754 | 3 |
| ENSG00000112902 | SEMA5A | 0.2184377 | 3 |
| ENSG00000165995 | CACNB2 | 0.1700655 | 3 |
| ENSG00000172780 | RAB43 | 0.1656377 | 3 |
| ENSG00000102974 | CTCF | 0.1270874 | 3 |
kable(table_data %>% filter(Module == top_modules[3] & `SFARI score` %in% c(1,2,3)) %>% dplyr::select(-Module),
caption=paste0('Top SFARI Genes for Module ', top_modules[3]))| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000136535 | TBR1 | -0.6536200 | 1 |
| ENSG00000136531 | SCN2A | -0.5374597 | 1 |
| ENSG00000145362 | ANK2 | -0.4367876 | 1 |
| ENSG00000036257 | CUL3 | -0.1935419 | 1 |
| ENSG00000174469 | CNTNAP2 | -0.7002690 | 2 |
| ENSG00000061676 | NCKAP1 | -0.6906433 | 2 |
| ENSG00000119866 | BCL11A | -0.3844250 | 2 |
| ENSG00000157445 | CACNA2D3 | -0.3393135 | 2 |
| ENSG00000144619 | CNTN4 | -0.3109404 | 2 |
| ENSG00000114861 | FOXP1 | 0.2551455 | 2 |
| ENSG00000166206 | GABRB3 | -0.1793562 | 2 |
| ENSG00000074590 | NUAK1 | -0.8235832 | 3 |
| ENSG00000144285 | SCN1A | -0.8000233 | 3 |
| ENSG00000196876 | SCN8A | -0.7887782 | 3 |
| ENSG00000170579 | DLGAP1 | -0.7789832 | 3 |
| ENSG00000132294 | EFR3A | -0.7500147 | 3 |
| ENSG00000078328 | RBFOX1 | -0.7311006 | 3 |
| ENSG00000003147 | ICA1 | -0.7126337 | 3 |
| ENSG00000197535 | MYO5A | -0.7116545 | 3 |
| ENSG00000175497 | DPP10 | -0.7059507 | 3 |
| ENSG00000182621 | PLCB1 | -0.6917670 | 3 |
| ENSG00000021645 | NRXN3 | -0.6715730 | 3 |
| ENSG00000166501 | PRKCB | -0.6151170 | 3 |
| ENSG00000139726 | DENR | -0.5936537 | 3 |
| ENSG00000142230 | SAE1 | -0.5499380 | 3 |
| ENSG00000183454 | GRIN2A | -0.5288764 | 3 |
| ENSG00000185345 | PARK2 | -0.4980373 | 3 |
| ENSG00000164506 | STXBP5 | -0.4854943 | 3 |
| ENSG00000182771 | GRID1 | -0.4477343 | 3 |
| ENSG00000132024 | CC2D1A | -0.4158439 | 3 |
| ENSG00000050030 | KIAA2022 | -0.4001380 | 3 |
| ENSG00000185008 | ROBO2 | -0.3078622 | 3 |
| ENSG00000182256 | GABRG3 | -0.2878943 | 3 |
| ENSG00000139174 | PRICKLE1 | -0.2154883 | 3 |
| ENSG00000134115 | CNTN6 | -0.1945703 | 3 |
| ENSG00000138411 | HECW2 | -0.1360240 | 3 |
| ENSG00000149970 | CNKSR2 | 0.0921242 | 3 |
| ENSG00000140945 | CDH13 | 0.0846523 | 3 |
| ENSG00000166147 | FBN1 | -0.0830677 | 3 |
| ENSG00000171723 | GPHN | -0.0744071 | 3 |
| ENSG00000149972 | CNTN5 | -0.0486109 | 3 |
kable(table_data %>% filter(Module == top_modules[4] & `SFARI score` %in% c(1,2,3)) %>% dplyr::select(-Module),
caption=paste0('Top SFARI Genes for Module ', top_modules[4]))| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000139613 | SMARCC2 | -0.4329730 | 2 |
| ENSG00000196557 | CACNA1H | -0.4127420 | 2 |
| ENSG00000171759 | PAH | -0.6896875 | 3 |
| ENSG00000169918 | OTUD7A | -0.4706156 | 3 |
| ENSG00000106290 | TAF6 | -0.4144602 | 3 |
| ENSG00000133026 | MYH10 | -0.3262873 | 3 |
| ENSG00000101489 | CELF4 | -0.3169738 | 3 |
| ENSG00000180914 | OXTR | -0.1668994 | 3 |
Modules with the strongest module-diagnosis correlation should have the highest percentage of SFARI Genes, but this doesn’t seem to be the case (even the opposite may be true)
plot_data = dataset %>% mutate('hasSFARIscore' = gene.score!='None') %>%
group_by(Module, MTcor, hasSFARIscore) %>% summarise(p=n()) %>%
left_join(dataset %>% group_by(Module) %>% summarise(n=n()), by='Module') %>%
mutate(p=round(p/n*100,2))
for(i in 1:nrow(plot_data)){
this_row = plot_data[i,]
if(this_row$hasSFARIscore==FALSE & this_row$p==100){
new_row = this_row
new_row$hasSFARIscore = TRUE
new_row$p = 0
plot_data = plot_data %>% rbind(new_row)
}
}
plot_data = plot_data %>% filter(hasSFARIscore==TRUE)
ggplotly(plot_data %>% ggplot(aes(MTcor, p, size=n)) + geom_smooth(color='gray', se=FALSE) +
geom_point(color=plot_data$Module, alpha=0.5, aes(id=Module)) + geom_hline(yintercept=mean(plot_data$p), color='gray') +
xlab('Module-Diagnosis correlation') + ylab('% of SFARI genes') +
theme_minimal() + theme(legend.position = 'none'))rm(i, this_row, new_row, plot_data)Breaking the SFARI genes by score
scores = c(1,2,3,4,5,6,'None')
plot_data = dataset %>% group_by(Module, MTcor, gene.score) %>% summarise(n=n()) %>%
left_join(dataset %>% group_by(Module) %>% summarise(N=n()), by='Module') %>%
mutate(p=round(n/N*100,2), gene.score = as.character(gene.score))
for(i in 1:nrow(plot_data)){
this_row = plot_data[i,]
if(sum(plot_data$Module == this_row$Module)<7){
missing_scores = which(! scores %in% plot_data$gene.score[plot_data$Module == this_row$Module])
for(s in missing_scores){
new_row = this_row
new_row$gene.score = as.character(s)
new_row$n = 0
new_row$p = 0
plot_data = plot_data %>% rbind(new_row)
}
}
}
plot_data = plot_data %>% filter(gene.score != 'None')
plot_function = function(i){
i = 2*i-1
plot_list = list()
for(j in 1:2){
plot_list[[j]] = ggplotly(plot_data %>% filter(gene.score==scores[i+j-1]) %>% ggplot(aes(MTcor, p, size=n)) +
geom_smooth(color='gray', se=FALSE) +
geom_point(color=plot_data$Module[plot_data$gene.score==scores[i+j-1]], alpha=0.5, aes(id=Module)) +
geom_hline(yintercept=mean(plot_data$p[plot_data$gene.score==scores[i+j-1]]), color='gray') +
xlab('Module-Diagnosis correlation') + ylab('% of SFARI genes') +
theme_minimal() + theme(legend.position = 'none'))
}
p = subplot(plot_list, nrows=1) %>% layout(annotations = list(
list(x = 0.2 , y = 1.05, text = paste0('SFARI score ', scores[i]), showarrow = F, xref='paper', yref='paper'),
list(x = 0.8 , y = 1.05, text = paste0('SFARI score ', scores[i+1]), showarrow = F, xref='paper', yref='paper')))
return(p)
}
plot_function(1)plot_function(2)plot_function(3)rm(i, s, this_row, new_row, plot_function)Module Eigengenes
Since these modules have the strongest relation to autism, this pattern should be reflected in their model eigengenes, having two different behaviours for the samples corresponding to autism and the ones corresponding to control.
In both cases, the Eigengenes separate the behaviour between autism and control samples very clearly!
plot_EGs = function(module){
plot_data = data.frame('ID' = rownames(MEs), 'MEs' = MEs[,paste0('ME',module)], 'Diagnosis' = datMeta$Diagnosis)
p = plot_data %>% ggplot(aes(Diagnosis, MEs, fill=Diagnosis)) + geom_boxplot() + theme_minimal() + theme(legend.position='none') +
ggtitle(paste0('Module ', module, ' (MTcor=',round(moduleTraitCor[paste0('ME',module),1],2),')'))
return(p)
}
p1 = plot_EGs(top_modules[1])
p2 = plot_EGs(top_modules[2])
p3 = plot_EGs(top_modules[3])
p4 = plot_EGs(top_modules[4])
grid.arrange(p1, p2, p3, p4, nrow=2)
rm(plot_EGs, p1, p2, p3, p4)Identifying important genes
Selecting the modules with the highest correlation to Diagnosis, and, from them, the genes with the highest module membership-(absolute) gene significance
*Ordered by \(\frac{MM+|GS|}{2}\)
There aren’t that many SFARI genes in the top genes of the modules and not a single one belonging to scores 1 and 2
create_table = function(module){
top_genes = dataset %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, paste0('MM.',gsub('#','',module)), GS, gene.score) %>%
filter(dataset$Module==module) %>% dplyr::rename('MM' = paste0('MM.',gsub('#','',module))) %>%
mutate(importance = (MM+abs(GS))/2) %>% arrange(by=-importance) %>% top_n(20)
return(top_genes)
}
top_genes = list()
for(i in 1:length(top_modules)) top_genes[[i]] = create_table(top_modules[i])
kable(top_genes[[1]], caption=paste0('Top 10 genes for module ', top_modules[1], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[1]),1],2),')'))| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000143384 | MCL1 | 0.8500241 | 0.9078060 | None | 0.8789151 |
| ENSG00000158615 | PPP1R15B | 0.8407717 | 0.8212537 | None | 0.8310127 |
| ENSG00000196935 | SRGAP1 | 0.7175838 | 0.9224538 | None | 0.8200188 |
| ENSG00000161638 | ITGA5 | 0.7550781 | 0.8597979 | None | 0.8074380 |
| ENSG00000148841 | ITPRIP | 0.7913310 | 0.8161802 | None | 0.8037556 |
| ENSG00000120278 | PLEKHG1 | 0.7857882 | 0.8086459 | None | 0.7972171 |
| ENSG00000133639 | BTG1 | 0.7819379 | 0.8082183 | None | 0.7950781 |
| ENSG00000101493 | ZNF516 | 0.6925114 | 0.8811710 | None | 0.7868412 |
| ENSG00000150457 | LATS2 | 0.7097060 | 0.8499030 | None | 0.7798045 |
| ENSG00000097007 | ABL1 | 0.7612757 | 0.7941289 | None | 0.7777023 |
| ENSG00000253731 | PCDHGA6 | 0.7450788 | 0.8036535 | None | 0.7743662 |
| ENSG00000106366 | SERPINE1 | 0.7535342 | 0.7911667 | 4 | 0.7723505 |
| ENSG00000198795 | ZNF521 | 0.7862020 | 0.7397450 | None | 0.7629735 |
| ENSG00000154640 | BTG3 | 0.6921295 | 0.8306872 | None | 0.7614084 |
| ENSG00000138166 | DUSP5 | 0.7541208 | 0.7643020 | None | 0.7592114 |
| ENSG00000072364 | AFF4 | 0.7413482 | 0.7716602 | 6 | 0.7565042 |
| ENSG00000204569 | PPP1R10 | 0.7470986 | 0.7645752 | None | 0.7558369 |
| ENSG00000135913 | USP37 | 0.6855044 | 0.8254166 | None | 0.7554605 |
| ENSG00000197329 | PELI1 | 0.7612944 | 0.7481606 | None | 0.7547275 |
| ENSG00000130222 | GADD45G | 0.6503873 | 0.8500145 | None | 0.7502009 |
kable(top_genes[[2]], caption=paste0('Top 10 genes for module ', top_modules[2], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[2]),1],2),')'))| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000003402 | CFLAR | 0.8349806 | 0.8167723 | None | 0.8258764 |
| ENSG00000138119 | MYOF | 0.8095979 | 0.8334501 | None | 0.8215240 |
| ENSG00000162745 | OLFML2B | 0.7794224 | 0.8526613 | None | 0.8160419 |
| ENSG00000089159 | PXN | 0.7551603 | 0.8687338 | None | 0.8119471 |
| ENSG00000124782 | RREB1 | 0.7602334 | 0.8444107 | None | 0.8023221 |
| ENSG00000198185 | ZNF334 | 0.8308122 | 0.7574061 | None | 0.7941092 |
| ENSG00000168769 | TET2 | 0.7660493 | 0.8170356 | 3 | 0.7915424 |
| ENSG00000120690 | ELF1 | 0.7858080 | 0.7870686 | None | 0.7864383 |
| ENSG00000163629 | PTPN13 | 0.7149171 | 0.8558172 | None | 0.7853671 |
| ENSG00000183808 | RBM12B | 0.6787244 | 0.8906825 | None | 0.7847035 |
| ENSG00000104870 | FCGRT | 0.6702519 | 0.8567333 | None | 0.7634926 |
| ENSG00000171988 | JMJD1C | 0.7726856 | 0.7508001 | 4 | 0.7617428 |
| ENSG00000117000 | RLF | 0.8221159 | 0.7009312 | None | 0.7615236 |
| ENSG00000173110 | HSPA6 | 0.7142783 | 0.8087644 | None | 0.7615214 |
| ENSG00000180596 | HIST1H2BC | 0.7506019 | 0.7717162 | None | 0.7611591 |
| ENSG00000102531 | FNDC3A | 0.7265045 | 0.7897408 | None | 0.7581227 |
| ENSG00000110719 | TCIRG1 | 0.6831133 | 0.8306871 | None | 0.7569002 |
| ENSG00000130066 | SAT1 | 0.7490607 | 0.7561901 | None | 0.7526254 |
| ENSG00000065978 | YBX1 | 0.6670100 | 0.8339125 | None | 0.7504612 |
| ENSG00000073792 | IGF2BP2 | 0.6578041 | 0.8387673 | None | 0.7482857 |
kable(top_genes[[3]], caption=paste0('Top 10 genes for module ', top_modules[3], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[3]),1],2),')'))| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000050748 | MAPK9 | 0.9018732 | -0.9400140 | None | 0.9209436 |
| ENSG00000108395 | TRIM37 | 0.9037848 | -0.9297931 | None | 0.9167889 |
| ENSG00000138078 | PREPL | 0.9001507 | -0.9038232 | None | 0.9019869 |
| ENSG00000177432 | NAP1L5 | 0.8683153 | -0.9048326 | None | 0.8865739 |
| ENSG00000155097 | ATP6V1C1 | 0.8612597 | -0.8966476 | None | 0.8789537 |
| ENSG00000114573 | ATP6V1A | 0.8322001 | -0.9172599 | None | 0.8747300 |
| ENSG00000163577 | EIF5A2 | 0.8346941 | -0.9108048 | None | 0.8727495 |
| ENSG00000128881 | TTBK2 | 0.8697142 | -0.8751661 | None | 0.8724401 |
| ENSG00000171132 | PRKCE | 0.8338660 | -0.9088992 | None | 0.8713826 |
| ENSG00000111674 | ENO2 | 0.8876355 | -0.8482909 | None | 0.8679632 |
| ENSG00000176490 | DIRAS1 | 0.8331173 | -0.8967040 | None | 0.8649106 |
| ENSG00000131437 | KIF3A | 0.8801280 | -0.8462276 | None | 0.8631778 |
| ENSG00000196876 | SCN8A | 0.9339299 | -0.7887782 | 3 | 0.8613540 |
| ENSG00000125814 | NAPB | 0.8865175 | -0.8311321 | None | 0.8588248 |
| ENSG00000172348 | RCAN2 | 0.7982100 | -0.9149243 | None | 0.8565672 |
| ENSG00000184368 | MAP7D2 | 0.8249437 | -0.8801442 | None | 0.8525440 |
| ENSG00000162694 | EXTL2 | 0.8007142 | -0.8982366 | None | 0.8494754 |
| ENSG00000130540 | SULT4A1 | 0.8815880 | -0.8113335 | None | 0.8464608 |
| ENSG00000144285 | SCN1A | 0.8893987 | -0.8000233 | 3 | 0.8447110 |
| ENSG00000132639 | SNAP25 | 0.8756003 | -0.8102863 | 4 | 0.8429433 |
kable(top_genes[[4]], caption=paste0('Top 10 genes for module ', top_modules[4], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[4]),1],2),')'))| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000106683 | LIMK1 | 0.8336101 | -0.8623672 | None | 0.8479886 |
| ENSG00000141576 | RNF157 | 0.8343007 | -0.8485893 | None | 0.8414450 |
| ENSG00000127838 | PNKD | 0.8393553 | -0.8333891 | None | 0.8363722 |
| ENSG00000140854 | KATNB1 | 0.8646680 | -0.7907368 | None | 0.8277024 |
| ENSG00000148334 | PTGES2 | 0.8037964 | -0.8316634 | None | 0.8177299 |
| ENSG00000006432 | MAP3K9 | 0.7465523 | -0.8872405 | None | 0.8168964 |
| ENSG00000130725 | UBE2M | 0.7814384 | -0.8501315 | None | 0.8157850 |
| ENSG00000105251 | SHD | 0.7661369 | -0.8630235 | None | 0.8145802 |
| ENSG00000139190 | VAMP1 | 0.8258018 | -0.8015636 | None | 0.8136827 |
| ENSG00000168993 | CPLX1 | 0.7956338 | -0.8267869 | None | 0.8112104 |
| ENSG00000183837 | PNMA3 | 0.7999070 | -0.8021580 | None | 0.8010325 |
| ENSG00000180155 | LYNX1 | 0.7934936 | -0.8012133 | None | 0.7973535 |
| ENSG00000124191 | TOX2 | 0.7739545 | -0.8098081 | None | 0.7918813 |
| ENSG00000139637 | C12orf10 | 0.7420227 | -0.8338129 | None | 0.7879178 |
| ENSG00000196972 | LINC00087 | 0.8285660 | -0.7454300 | None | 0.7869980 |
| ENSG00000135631 | RAB11FIP5 | 0.7285209 | -0.8340525 | 4 | 0.7812867 |
| ENSG00000174238 | PITPNA | 0.6804206 | -0.8774836 | None | 0.7789521 |
| ENSG00000106789 | CORO2A | 0.7260306 | -0.8209005 | None | 0.7734656 |
| ENSG00000196177 | ACADSB | 0.7340860 | -0.8051786 | None | 0.7696323 |
| ENSG00000127445 | PIN1 | 0.7966748 | -0.7363993 | None | 0.7665370 |
rm(create_table)pca = datExpr %>% prcomp
ids = c()
for(tg in top_genes) ids = c(ids, tg$ID)
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>% dplyr::select(ID, PC1, PC2, Module, gene.score) %>%
mutate(color = ifelse(Module %in% top_modules, as.character(Module), 'gray')) %>%
mutate(alpha = ifelse(color %in% top_modules &
ID %in% ids, 1, 0.1))
plot_data %>% ggplot(aes(PC1, PC2)) + geom_point(alpha=plot_data$alpha, color=plot_data$color) +
theme_minimal() + ggtitle('Important genes identified through WGCNA')
Level of expression by Diagnosis for top genes, ordered by importance (defined above)
create_plot = function(i){
plot_data = datExpr[rownames(datExpr) %in% top_genes[[i]]$ID,] %>% mutate('ID' = rownames(.)) %>%
melt(id.vars='ID') %>% mutate(variable = gsub('X','',variable)) %>%
left_join(top_genes[[i]], by='ID') %>%
left_join(datMeta %>% dplyr::select(Dissected_Sample_ID, Diagnosis),
by = c('variable'='Dissected_Sample_ID')) %>% arrange(desc(importance))
p = ggplotly(plot_data %>% mutate(external_gene_id=factor(external_gene_id,
levels=unique(plot_data$external_gene_id), ordered=T)) %>%
ggplot(aes(external_gene_id, value, fill=Diagnosis)) + geom_boxplot() + theme_minimal() +
xlab(paste0('Top genes for module ', top_modules[i], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[i]][1],2), ')')) + ylab('Level of Expression') +
theme(axis.text.x = element_text(angle = 90, hjust = 1)))
return(p)
}
create_plot(1)create_plot(2)create_plot(3)create_plot(4)rm(create_plot)Enrichment Analysis
Using the package anRichment
It was designed by Peter Langfelder explicitly to perform enrichmen analysis on WGCNA’s modules in brain-related experiments (mainly Huntington’s Disease)
It has packages with brain annotations:
BrainDiseaseCollection: A Brain Disease Gene Set Collection for anRichment
MillerAIBSCollection: (included in anRichment) Contains gene sets collected by Jeremy A. Miller at AIBS of various cell type and brain region marker sets, gene sets collected from expression studies of developing brain, as well as a collection of transcription factor (TF) targets from the original ChEA study
The tutorial says it’s an experimental package
It’s not on CRAN nor in Bioconductor
# Prepare dataset
# Create dataset with top modules membership and removing the genes without an assigned module
EA_dataset = data.frame('ensembl_gene_id' = genes_info$ID,
module = ifelse(genes_info$Module %in% top_modules, genes_info$Module, 'other')) %>%
filter(genes_info$Module!='gray')
# Assign Entrez Gene Id to each gene
getinfo = c('ensembl_gene_id','entrezgene')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl', host='feb2014.archive.ensembl.org')
biomart_output = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=EA_dataset$ensembl_gene_id, mart=mart)## Cache foundEA_dataset = EA_dataset %>% left_join(biomart_output, by='ensembl_gene_id')
for(tm in top_modules){
cat(paste0('\n',sum(EA_dataset$module==tm & is.na(EA_dataset$entrezgene)), ' genes from top module ',
tm, ' don\'t have an Entrez Gene ID'))
}##
## 27 genes from top module #73B000 don't have an Entrez Gene ID
## 29 genes from top module #EE8045 don't have an Entrez Gene ID
## 27 genes from top module #00B9E3 don't have an Entrez Gene ID
## 25 genes from top module #84AD00 don't have an Entrez Gene IDrm(getinfo, mart, biomart_output, tm)# Manual: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/GeneAnnotation/Tutorials/anRichment-Tutorial1.pdf
collectGarbage()
# EA_dataset = rbind(EA_dataset[EA_dataset$module!='other',], EA_dataset[EA_dataset$module=='other',][sample(sum(EA_dataset$module=='other'), 1000),])
# Prepare datasets
GO_col = buildGOcollection(organism = 'human', verbose = 0)## Loading required package: org.Hs.eg.db## ## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columnsinternal_col = internalCollection(organism = 'human')
MillerAIBS_col = MillerAIBSCollection(organism = 'human')
BrainDisease_col = BrainDiseaseCollection(organism = 'human')
combined_col = mergeCollections(GO_col, internal_col, MillerAIBS_col, BrainDisease_col)
# Print collections used
cat('Using collections: ')## Using collections:knownGroups(combined_col, sortBy = 'size')## [1] "GO"
## [2] "GO.BP"
## [3] "GO.MF"
## [4] "GO.CC"
## [5] "JA Miller at AIBS"
## [6] "Chip-X enrichment analysis (ChEA)"
## [7] "Brain"
## [8] "JAM"
## [9] "Prenatal brain"
## [10] "Brain region markers"
## [11] "Cortex"
## [12] "Brain region marker enriched gene sets"
## [13] "WGCNA"
## [14] "BrainRegionMarkers"
## [15] "BrainRegionMarkers.HBA"
## [16] "BrainRegionMarkers.HBA.localMarker(top200)"
## [17] "Postnatal brain"
## [18] "ImmunePathways"
## [19] "Markers of cortex layers"
## [20] "BrainLists"
## [21] "Cell type markers"
## [22] "Germinal brain"
## [23] "BrainRegionMarkers.HBA.globalMarker(top200)"
## [24] "Accelerated evolution"
## [25] "Postmitotic brain"
## [26] "BrainLists.Blalock_AD"
## [27] "BrainLists.DiseaseGenes"
## [28] "BloodAtlases"
## [29] "Verge Disease Genes"
## [30] "BloodAtlases.Whitney"
## [31] "BrainLists.JAXdiseaseGene"
## [32] "BrainLists.MO"
## [33] "Age-associated genes"
## [34] "BrainLists.Lu_Aging"
## [35] "Cell type marker enriched gene sets"
## [36] "BrainLists.CA1vsCA3"
## [37] "BrainLists.MitochondrialType"
## [38] "BrainLists.MO.2+_26Mar08"
## [39] "BrainLists.MO.Sugino"
## [40] "BloodAtlases.Gnatenko2"
## [41] "BloodAtlases.Kabanova"
## [42] "BrainLists.Voineagu"
## [43] "StemCellLists"
## [44] "StemCellLists.Lee"# Perform Enrichment Analysis
enrichment = enrichmentAnalysis(classLabels = EA_dataset$module, identifiers = EA_dataset$entrezgene,
refCollection = combined_col, #useBackground = 'given',
threshold = 1e-4, thresholdType = 'Bonferroni',
getOverlapEntrez = FALSE, getOverlapSymbols = TRUE)## enrichmentAnalysis: preparing data..
## ..working on label set 1 ..Results
kable(enrichment$enrichmentTable %>% filter(class==top_modules[1]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio,
effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[1], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[1]][1],4), ')'))| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAM:003078 | Temporal Lobe_IN_Cerebral Cortex | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.0059431 | 0.0000369 | 2.589589 | 1425 | 158 | 37 |
| JAMiller.AIBS.000169 | Cerebellum-enriched genes in midfetal human brain | JA Miller at AIBS|Brain|Prenatal brain|Brain region markers | 0.0148439 | 0.0000858 | 2.764561 | 1425 | 124 | 31 |
| GO:0072359 | circulatory system development | GO|GO.BP | 0.0156674 | 0.0000893 | 1.556171 | 1425 | 938 | 132 |
| GO:0072358 | cardiovascular system development | GO|GO.BP | 0.0255755 | 0.0001371 | 1.688960 | 1425 | 622 | 95 |
| GO:0001944 | vasculature development | GO|GO.BP | 0.0468149 | 0.0002284 | 1.677678 | 1425 | 613 | 93 |
| GO:0001568 | blood vessel development | GO|GO.BP | 0.1391834 | 0.0005990 | 1.663463 | 1425 | 585 | 88 |
| JAMiller.AIBS.000502 | Genes bound by SUZ12 in mouse MESC from PMID 16625203 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.1891141 | 0.0007751 | 1.551789 | 1425 | 791 | 111 |
| JAMiller.AIBS.000138 | VZ/SZ/IZ enriched in E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Germinal brain | 0.1944664 | 0.0007905 | 1.898554 | 1425 | 332 | 57 |
| JAMiller.AIBS.000208 | RegionalWGCNA midfetal M38 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.2263597 | 0.0008912 | 1.507943 | 1425 | 902 | 123 |
| JAM:002886 | Hypothalamus | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.2992571 | 0.0011335 | 2.431246 | 1425 | 141 | 31 |
kable(enrichment$enrichmentTable %>% filter(class==top_modules[2]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio,
effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, enrichmentRatio),
caption = paste0('Enriched terms for module ', top_modules[2], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[2]][1],4), ')'))| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000176 | CortexWGCNA midfetal M6 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.0001115 | 1.20e-06 | 1.933365 | 850 | 863 | 90 |
| GO:0010556 | regulation of macromolecule biosynthetic process | GO|GO.BP | 0.0006070 | 5.30e-06 | 1.375427 | 850 | 3464 | 257 |
| GO:1903506 | regulation of nucleic acid-templated transcription | GO|GO.BP | 0.0006810 | 5.90e-06 | 1.411367 | 850 | 3008 | 229 |
| GO:0010468 | regulation of gene expression | GO|GO.BP | 0.0008402 | 6.90e-06 | 1.344785 | 850 | 3860 | 280 |
| GO:2001141 | regulation of RNA biosynthetic process | GO|GO.BP | 0.0008644 | 7.00e-06 | 1.407623 | 850 | 3016 | 229 |
| GO:0009889 | regulation of biosynthetic process | GO|GO.BP | 0.0019537 | 1.41e-05 | 1.346451 | 850 | 3690 | 268 |
| GO:0051252 | regulation of RNA metabolic process | GO|GO.BP | 0.0020681 | 1.48e-05 | 1.377692 | 850 | 3243 | 241 |
| GO:0097659 | nucleic acid-templated transcription | GO|GO.BP | 0.0026497 | 1.79e-05 | 1.381739 | 850 | 3153 | 235 |
| GO:0006355 | regulation of transcription, DNA-templated | GO|GO.BP | 0.0027739 | 1.86e-05 | 1.397619 | 850 | 2958 | 223 |
| GO:0031326 | regulation of cellular biosynthetic process | GO|GO.BP | 0.0028170 | 1.88e-05 | 1.346881 | 850 | 3620 | 263 |
kable(enrichment$enrichmentTable %>% filter(class==top_modules[3]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio,
effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[3], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[3]][1],4), ')'))| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000052 | CortexWGCNA 15-21 post-conception weeks C26 | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 0.00e+00 | 0e+00 | 2.707335 | 1701 | 722 | 211 |
| JAMiller.AIBS.000142 | Highest in CP of 13-16 post-conception weeks human | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Postmitotic brain | 0.00e+00 | 0e+00 | 2.191886 | 1701 | 1213 | 287 |
| JAMiller.AIBS.000569 | WGCNA humanSpecificOlivedrab2Module frontalCtx FOXP2 | JA Miller at AIBS|Brain|Postnatal brain|Cortex|WGCNA | 0.00e+00 | 0e+00 | 1.523000 | 1701 | 4045 | 665 |
| JAM:002769 | downAD_mitochondrion | JAM|BrainLists|BrainLists.Blalock_AD | 0.00e+00 | 0e+00 | 3.356002 | 1701 | 265 | 96 |
| JAMiller.AIBS.000150 | Highest in CP of E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Postmitotic brain | 0.00e+00 | 0e+00 | 1.874676 | 1701 | 1270 | 257 |
| JAM:003016 | downAD_synapticTransmission | JAM|BrainLists|BrainLists.Blalock_AD | 0.00e+00 | 0e+00 | 5.158343 | 1701 | 88 | 49 |
| JAMiller.AIBS.000155 | Lowest in VZ of E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex | 0.00e+00 | 0e+00 | 1.721720 | 1701 | 1668 | 310 |
| JAMiller.AIBS.000123 | HippocampusWGCNA turquoise DGenriched upAge | JA Miller at AIBS|Brain|Postnatal brain|WGCNA | 0.00e+00 | 0e+00 | 1.916682 | 1701 | 1102 | 228 |
| JAMiller.AIBS.000570 | WGCNA Olivedrab2ModuleGenes with enriched ELAVL2 targets | JA Miller at AIBS|Brain|Postnatal brain|Cortex|WGCNA | 0.00e+00 | 0e+00 | 2.474226 | 1701 | 483 | 129 |
| GO:0045202 | synapse | GO|GO.CC | 0.00e+00 | 0e+00 | 1.839475 | 1701 | 1113 | 221 |
| GO:0044456 | synapse part | GO|GO.CC | 0.00e+00 | 0e+00 | 1.942109 | 1701 | 892 | 187 |
| JAM:003072 | Tail of Caudate Nucleus_IN_Striatum | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 3.509949 | 1701 | 161 | 61 |
| GO:0097458 | neuron part | GO|GO.CC | 0.00e+00 | 0e+00 | 1.643978 | 1701 | 1606 | 285 |
| GO:0097060 | synaptic membrane | GO|GO.CC | 0.00e+00 | 0e+00 | 2.389246 | 1701 | 411 | 106 |
| JAMiller.AIBS.000141 | CP enriched in E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Postmitotic brain | 0.00e+00 | 0e+00 | 2.107674 | 1701 | 567 | 129 |
| JAM:002764 | downAging_mitochondria_synapse | JAM|BrainLists|BrainLists.Lu_Aging | 0.00e+00 | 0e+00 | 2.351621 | 1701 | 390 | 99 |
| JAM:002751 | Basal Pons | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 3.180999 | 1701 | 166 | 57 |
| GO:0099536 | synaptic signaling | GO|GO.BP | 0.00e+00 | 0e+00 | 1.981063 | 1701 | 650 | 139 |
| GO:0007268 | chemical synaptic transmission | GO|GO.BP | 0.00e+00 | 0e+00 | 1.966407 | 1701 | 636 | 135 |
| GO:0098916 | anterograde trans-synaptic signaling | GO|GO.BP | 0.00e+00 | 0e+00 | 1.966407 | 1701 | 636 | 135 |
| GO:0099537 | trans-synaptic signaling | GO|GO.BP | 0.00e+00 | 0e+00 | 1.956365 | 1701 | 644 | 136 |
| JAM:002744 | Autism_differential_expression_across_at_least_one_comparison | JAM|BrainLists|BrainLists.Voineagu | 0.00e+00 | 0e+00 | 1.855218 | 1701 | 764 | 153 |
| JAM:003054 | subiculum_IN_Hippocampal Formation | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 3.031842 | 1701 | 165 | 54 |
| GO:0034702 | ion channel complex | GO|GO.CC | 0.00e+00 | 0e+00 | 2.514012 | 1701 | 269 | 73 |
| GO:0043005 | neuron projection | GO|GO.CC | 0.00e+00 | 0e+00 | 1.633021 | 1701 | 1214 | 214 |
| JAMiller.AIBS.000005 | CPi markers at 21 post-conception weeks | JA Miller at AIBS|Brain|Prenatal brain|Cortex|Markers of cortex layers|Postmitotic brain | 0.00e+00 | 0e+00 | 2.383013 | 1701 | 311 | 80 |
| GO:1902495 | transmembrane transporter complex | GO|GO.CC | 0.00e+00 | 0e+00 | 2.444657 | 1701 | 288 | 76 |
| GO:0030424 | axon | GO|GO.CC | 0.00e+00 | 0e+00 | 1.968995 | 1701 | 574 | 122 |
| GO:0045211 | postsynaptic membrane | GO|GO.CC | 0.00e+00 | 0e+00 | 2.376146 | 1701 | 308 | 79 |
| JAM:002805 | Cerebral Cortex | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 2.992087 | 1701 | 161 | 52 |
| JAM:002739 | arcuate nucleus of medulla_IN_Myelencephalon | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 2.940060 | 1701 | 167 | 53 |
| GO:1990351 | transporter complex | GO|GO.CC | 0.00e+00 | 0e+00 | 2.378585 | 1701 | 296 | 76 |
| GO:0005216 | ion channel activity | GO|GO.MF | 0.00e+00 | 0e+00 | 2.226422 | 1701 | 362 | 87 |
| JAM:002920 | Lateral Nucleus_IN_Amygdala | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 2.919552 | 1701 | 165 | 52 |
| GO:0098793 | presynapse | GO|GO.CC | 1.00e-07 | 0e+00 | 2.049727 | 1701 | 461 | 102 |
| GO:0022838 | substrate-specific channel activity | GO|GO.MF | 1.00e-07 | 0e+00 | 2.178283 | 1701 | 370 | 87 |
| GO:0034220 | ion transmembrane transport | GO|GO.BP | 2.00e-07 | 0e+00 | 1.648519 | 1701 | 1034 | 184 |
| GO:0022890 | inorganic cation transmembrane transporter activity | GO|GO.MF | 2.00e-07 | 0e+00 | 1.964537 | 1701 | 514 | 109 |
| GO:0015318 | inorganic molecular entity transmembrane transporter activity | GO|GO.MF | 3.00e-07 | 0e+00 | 1.802788 | 1701 | 704 | 137 |
| JAMiller.AIBS.000095 | Cortical PNOC neurons | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers|Cortex | 3.00e-07 | 0e+00 | 1.280862 | 1701 | 3949 | 546 |
| GO:0022803 | passive transmembrane transporter activity | GO|GO.MF | 3.00e-07 | 0e+00 | 2.124981 | 1701 | 388 | 89 |
| JAM:002882 | Hippocampal Formation | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 4.00e-07 | 0e+00 | 2.812274 | 1701 | 168 | 51 |
| GO:0006812 | cation transport | GO|GO.BP | 5.00e-07 | 0e+00 | 1.648359 | 1701 | 1006 | 179 |
| GO:0022839 | ion gated channel activity | GO|GO.MF | 5.00e-07 | 0e+00 | 2.308086 | 1701 | 293 | 73 |
| GO:0015267 | channel activity | GO|GO.MF | 7.00e-07 | 0e+00 | 2.106534 | 1701 | 387 | 88 |
| GO:0022836 | gated channel activity | GO|GO.MF | 8.00e-07 | 0e+00 | 2.269977 | 1701 | 302 | 74 |
| JAMiller.AIBS.000463 | Genes bound by SMAD4 in HUMAN A2780 from PMID 21799915 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1.00e-06 | 0e+00 | 1.414957 | 1701 | 2082 | 318 |
| GO:0008324 | cation transmembrane transporter activity | GO|GO.MF | 1.70e-06 | 0e+00 | 1.882784 | 1701 | 556 | 113 |
| GO:0034703 | cation channel complex | GO|GO.CC | 2.30e-06 | 0e+00 | 2.555576 | 1701 | 203 | 56 |
| JAMiller.AIBS.000042 | CortexWGCNA 15-21 post-conception weeks C16 SPenriched | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 2.60e-06 | 0e+00 | 2.573323 | 1701 | 198 | 55 |
| GO:0098660 | inorganic ion transmembrane transport | GO|GO.BP | 2.70e-06 | 0e+00 | 1.750569 | 1701 | 725 | 137 |
| GO:0005261 | cation channel activity | GO|GO.MF | 4.60e-06 | 1e-07 | 2.266714 | 1701 | 282 | 69 |
| GO:0015672 | monovalent inorganic cation transport | GO|GO.BP | 4.90e-06 | 1e-07 | 1.967567 | 1701 | 452 | 96 |
| JAM:002824 | Dentate Nucleus_IN_Cerebellar Nucleus | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 6.60e-06 | 1e-07 | 2.711404 | 1701 | 164 | 48 |
| GO:0098655 | cation transmembrane transport | GO|GO.BP | 6.60e-06 | 1e-07 | 1.721512 | 1701 | 748 | 139 |
| GO:0098794 | postsynapse | GO|GO.CC | 8.20e-06 | 1e-07 | 1.821389 | 1701 | 590 | 116 |
| JAM:002918 | lateral medullary reticular group_IN_Myelencephalon | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 1.15e-05 | 2e-07 | 2.704387 | 1701 | 161 | 47 |
| GO:0015075 | ion transmembrane transporter activity | GO|GO.MF | 1.73e-05 | 2e-07 | 1.698800 | 1701 | 758 | 139 |
| GO:0015077 | monovalent inorganic cation transmembrane transporter activity | GO|GO.MF | 1.82e-05 | 2e-07 | 2.135617 | 1701 | 321 | 74 |
| GO:0099240 | intrinsic component of synaptic membrane | GO|GO.CC | 2.77e-05 | 4e-07 | 2.707002 | 1701 | 154 | 45 |
| GO:0098662 | inorganic cation transmembrane transport | GO|GO.BP | 2.78e-05 | 4e-07 | 1.748449 | 1701 | 657 | 124 |
| GO:0006811 | ion transport | GO|GO.BP | 4.13e-05 | 5e-07 | 1.468754 | 1701 | 1457 | 231 |
| JAMiller.AIBS.000503 | Genes bound by SUZ12 in mouse MESC from PMID 18555785 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 4.86e-05 | 6e-07 | 1.677725 | 1701 | 762 | 138 |
| GO:0120025 | plasma membrane bounded cell projection | GO|GO.CC | 6.43e-05 | 7e-07 | 1.393951 | 1701 | 1914 | 288 |
| JAM:002991 | downAD_synapticTransmission | JAM|BrainLists|BrainLists.Blalock_AD | 9.44e-05 | 1e-06 | 3.151657 | 1701 | 97 | 33 |
kable(enrichment$enrichmentTable %>% filter(class==top_modules[4]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio,
effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[4], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[4]][1],4), ')'))| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000124 | HippocampusWGCNA yellow | JA Miller at AIBS|Brain|Postnatal brain|WGCNA | 0.0000038 | 0.0000001 | 2.175347 | 856 | 677 | 80 |
| GO:0005759 | mitochondrial matrix | GO|GO.CC | 0.0452945 | 0.0002231 | 2.105051 | 856 | 446 | 51 |
| JAMiller.AIBS.000048 | CortexWGCNA 15-21 post-conception weeks C22 CPenriched enrichedForAutismGenes | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 0.1443667 | 0.0006143 | 1.877221 | 856 | 608 | 62 |
| JAMiller.AIBS.000206 | RegionalWGCNA midfetal M36 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.2920425 | 0.0011147 | 2.492032 | 856 | 229 | 31 |
| GO:0098798 | mitochondrial protein complex | GO|GO.CC | 0.3202003 | 0.0012038 | 2.401158 | 856 | 253 | 33 |
| GO:0044429 | mitochondrial part | GO|GO.CC | 0.5590261 | 0.0019303 | 1.625425 | 856 | 974 | 86 |
| JAM:002985 | Parietal Lobe_IN_Cerebral Cortex | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 1.0000000 | 0.0076641 | 3.012362 | 856 | 110 | 18 |
| GO:0034470 | ncRNA processing | GO|GO.BP | 1.0000000 | 0.0067087 | 2.028097 | 856 | 354 | 39 |
| JAMiller.AIBS.000044 | CortexWGCNA 15-21 post-conception weeks C18 | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 1.0000000 | 0.0102615 | 1.664224 | 856 | 719 | 65 |
| GO:0005739 | mitochondrion | GO|GO.CC | 1.0000000 | 0.0047036 | 1.464200 | 856 | 1471 | 117 |
Save Enrichment Analysis results
save(enrichment, file='./../Data/enrichmentAnalysis.RData')
#load('./../Data/enrichmentAnalysis.RData')Session info
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Scientific Linux 7.6 (Nitrogen)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Hs.eg.db_3.10.0
## [2] BrainDiseaseCollection_1.00
## [3] anRichment_1.01-2
## [4] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
## [5] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [6] GenomicFeatures_1.38.2
## [7] GenomicRanges_1.38.0
## [8] GenomeInfoDb_1.22.0
## [9] anRichmentMethods_0.90-1
## [10] WGCNA_1.68
## [11] fastcluster_1.1.25
## [12] dynamicTreeCut_1.63-1
## [13] GO.db_3.10.0
## [14] AnnotationDbi_1.48.0
## [15] IRanges_2.20.2
## [16] S4Vectors_0.24.3
## [17] Biobase_2.46.0
## [18] BiocGenerics_0.32.0
## [19] biomaRt_2.42.0
## [20] knitr_1.24
## [21] doParallel_1.0.15
## [22] iterators_1.0.12
## [23] foreach_1.4.7
## [24] polycor_0.7-10
## [25] expss_0.10.1
## [26] GGally_1.4.0
## [27] gridExtra_2.3
## [28] viridis_0.5.1
## [29] viridisLite_0.3.0
## [30] RColorBrewer_1.1-2
## [31] dendextend_1.13.3
## [32] plotly_4.9.2
## [33] glue_1.3.1
## [34] reshape2_1.4.3
## [35] forcats_0.4.0
## [36] stringr_1.4.0
## [37] dplyr_0.8.3
## [38] purrr_0.3.3
## [39] readr_1.3.1
## [40] tidyr_1.0.2
## [41] tibble_2.1.3
## [42] ggplot2_3.2.1
## [43] tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.5
## [3] Hmisc_4.2-0 BiocFileCache_1.10.2
## [5] plyr_1.8.5 lazyeval_0.2.2
## [7] splines_3.6.0 crosstalk_1.0.0
## [9] BiocParallel_1.20.1 robust_0.4-18.2
## [11] digest_0.6.24 htmltools_0.4.0
## [13] fansi_0.4.1 magrittr_1.5
## [15] checkmate_1.9.4 memoise_1.1.0
## [17] fit.models_0.5-14 cluster_2.0.8
## [19] annotate_1.64.0 Biostrings_2.54.0
## [21] modelr_0.1.5 matrixStats_0.55.0
## [23] askpass_1.1 prettyunits_1.0.2
## [25] colorspace_1.4-1 blob_1.2.1
## [27] rvest_0.3.5 rappdirs_0.3.1
## [29] rrcov_1.4-7 haven_2.2.0
## [31] xfun_0.8 crayon_1.3.4
## [33] RCurl_1.95-4.12 jsonlite_1.6
## [35] genefilter_1.68.0 impute_1.60.0
## [37] survival_2.44-1.1 gtable_0.3.0
## [39] zlibbioc_1.32.0 XVector_0.26.0
## [41] DelayedArray_0.12.2 DEoptimR_1.0-8
## [43] scales_1.1.0 mvtnorm_1.0-11
## [45] DBI_1.1.0 Rcpp_1.0.3
## [47] xtable_1.8-4 progress_1.2.2
## [49] htmlTable_1.13.1 foreign_0.8-71
## [51] bit_1.1-15.2 preprocessCore_1.48.0
## [53] Formula_1.2-3 htmlwidgets_1.5.1
## [55] httr_1.4.1 ellipsis_0.3.0
## [57] acepack_1.4.1 farver_2.0.3
## [59] pkgconfig_2.0.3 reshape_0.8.8
## [61] XML_3.99-0.3 nnet_7.3-12
## [63] dbplyr_1.4.2 locfit_1.5-9.1
## [65] later_1.0.0 labeling_0.3
## [67] tidyselect_0.2.5 rlang_0.4.4
## [69] munsell_0.5.0 cellranger_1.1.0
## [71] tools_3.6.0 cli_2.0.1
## [73] generics_0.0.2 RSQLite_2.2.0
## [75] broom_0.5.4 fastmap_1.0.1
## [77] evaluate_0.14 yaml_2.2.0
## [79] bit64_0.9-7 fs_1.3.1
## [81] robustbase_0.93-5 nlme_3.1-139
## [83] mime_0.9 xml2_1.2.2
## [85] compiler_3.6.0 rstudioapi_0.10
## [87] curl_4.3 reprex_0.3.0
## [89] geneplotter_1.64.0 pcaPP_1.9-73
## [91] stringi_1.4.6 highr_0.8
## [93] lattice_0.20-38 Matrix_1.2-17
## [95] vctrs_0.2.2 pillar_1.4.3
## [97] lifecycle_0.1.0 data.table_1.12.8
## [99] bitops_1.0-6 httpuv_1.5.2
## [101] rtracklayer_1.46.0 R6_2.4.1
## [103] latticeExtra_0.6-28 promises_1.1.0
## [105] codetools_0.2-16 MASS_7.3-51.4
## [107] assertthat_0.2.1 SummarizedExperiment_1.16.1
## [109] DESeq2_1.26.0 openssl_1.4.1
## [111] withr_2.1.2 GenomicAlignments_1.22.1
## [113] Rsamtools_2.2.2 GenomeInfoDbData_1.2.2
## [115] hms_0.5.3 grid_3.6.0
## [117] rpart_4.1-15 rmarkdown_1.14
## [119] Cairo_1.5-10 shiny_1.4.0
## [121] lubridate_1.7.4 base64enc_0.1-3